We tested for tradeoffs among parameters
in an exponential seed dispersal function using six hypothetical data sets (AF)
generated from a hypothetical 1 km2 source area of trees and 200
1-m2 quadrats distributed in two transects through a central 1 ha
portion of the source area. Simulated seed rain was generated as a Poisson
process, with mean seed rain specified by:

where dbhiand
disti are the size and distance to i = 1..n trees within
a 50 m radius of a seed trap, and
is a normalizer that equals the arcwise integration of the dispersal kernel. We
generated six data sets (AF) with different combinations of parameters
(Table 1). We then used maximum-likelihood estimation with simulated annealing
to test our ability to accurately estimate the underlying parameter values [A(est.)
 F(est.)]. We also report 2-unit support intervals (SI)
as measures of the strength of evidence for the parameter estimates. The
shapes of the true and estimated dispersal functions are illustrated in Fig.
1 of the manuscript.

TABLE A1. Parameter values
used to generate datasets AF, and the values estimated using maximum likelihood
methods [A(est.)  F(est.)], along with 2-unit support intervals (SI)
as a measure of the strength of evidence for the parameter estimates.